This study explains about a serial smart production system where a single-type of product is produced. This system uses an unequally sized batch policy in subsequent stages. The setup cost is not always deterministic, it can be controllable and reduced by increasing the capital investment cost, and that the production rates in the system may vary within given limits across batches of shipments. Furthermore, as imperfect items are produced in long-run system, to clean the imperfectness autonomation policy is adopted for inspection, which make the process smarter. The shipment lot sizes of the deliveries are unequal and variable. In long-run production system, defective items are produced in “out-of-control” state. In this model, the defect rate is random with a uniform distribution which is clean from the system by autonomation. In addition, in the remanufacturing process, it is assuming that all defective products are repaired, and no defective products are scrapped. The main theme of developing this model is to determine the number of shipments and the optimal production lot size to adjust the production rates and decrease the total system cost under a reduced setup cost by considering the discrete investment and make a serial smart production system. A solution procedure along with an advanced algorithm was proposed for solving the model. Numerical examples with some graphical representations are provided to validate the model.

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Evaluating the assemblability at the design stage is important because of the cost competitiveness of company. However by compared results of the sequence of bottle neck process of production stage, the Lucas DFA (Design for assembly) and Boothroyd & Dewhurst DFA is not enough to evaluate the assenblability accurately. Therefore, in this paper, we proposed three design factors which is affecting assembly but not mentioned at DFA. These design factors would help the designers to evaluate assemblability and improve the productivity.

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Aiming at the features of the common Supply Chain quality management, an intelligent Supply Chain quality management system based on Six Sigma theory was proposed. The Six Sigma workflow, organizational framework and system goal were adopted in the system overall design, and some traditional Six Sigma tools were integrated into the system. A sub-system for expansive intelligent Six Sigma expert model was designed, which solved the problem of knowledge acquisition in the general expert system and endowed the system with the characteristic of local intelligence.